New York University Polytechnic School of Engineering professor Maurizio Porfiri led an international team of researchers in a study that applies machine learning to the understanding of social behavior in animal species. The researchers created a framework to apply humans’ innate ability to recognize behavior patterns to machine-learning techniques. The team used an existing machine-learning method called isometric mapping (ISOMAP) to determine if the algorithm could analyze video footage of a flock of flying birds, register the aligned motion, and embed the information on a low-dimensional manifold to visually display the properties of the behavior. “We wanted to put ISOMAP to the test alongside human observation,” Porfiri says. “If humans and computers could observe social animal species and arrive at similar characterizations of their behavior, we would have a dramatically better quantitative tool for exploring collective animal behavior than anything we’ve seen.” Using video of five social species, including ants, fish, frogs, chickens, and humans, the team compared human rankings to the ISOMAP manifolds, and found that results were highly similar. The team believes the work represents a breakthrough in understanding social animal behaviors, and will conduct further research on more subtle aspects of collective behavior, such as the chirping of crickets.